The success or failure of your data-driven business initiatives relies on your ability to trust your data. But as data volumes grow, it becomes a major challenge to understand, measure, monitor, cleanse, and govern all that data. Join this session to learn key metrics and steps you can take to kickstart a data quality strategy.

1. Kickstart a data
quality strategy to
build trust in data
Aaron Wallace
Principal Product Manager

2. Accuracy
Consistency
Context
Data
integrity is

3. Trust
Data
integrity is

4. Trusted data requires
Understanding
Measuring
Monitoring
Cleansing
Governing

5. Data integrity is
a journey
• Business & IT are collaborating more than ever –
no more corporate IT-driven initiatives
• Every organization is on their own unique
journey to data integrity
• Your business goals & use cases define your
discrete steps along the journey

6. Deliver on two key imperatives - often perceived as
contradictory across various use cases:
GROW PROTECT
COVID has accelerated the drive to all things digital
CRM
Optimization
Digital
Transformation
Governance
Risk Compliance
Channel
Optimization/
Segmentation
Omnichannel
Experience
Financial Crimes
& Compliance
Data Privacy
Regulations
Fraud
Data Democratization
and Literacy
Downstream Analytics,
AI and ML

7. More data,
more problems?
• The trend toward better analytics and BI tools
requires more agile use of first party data
• Need for new governance practices, data
democratization and data literacy
• Though data is distributed across silos and
difficult to organize
• And heavyweight batch processes fail to take
advantage of modern data platforms

8. Challenges
47% 66%
Source: * Harvard Business Review &TOP 10 DATA
MANAGEMENT TRENDS FOR 2020 - Experian
of newly created data
records have at least
one critical error*
of organizations say a backlog
of data debt negatively impacts
new data initiatives like AI,
machine learning, or analytics
28% 77%
of organizations believes that
of their current customer/
prospect data is inaccurate
in some way
of organizations are actively
working to put data insights
into the hands of more
people across the business

9. Data Quality Dimensions
DQ Dimension How it’s evaluated
Accuracy Factualness. Agree with real-world, match to agreed source
Consistency Free of conflict with other instances
Precision Precision of data value
Timeliness Time expectation for availability, concurrence of distributed data
Currency Current with world it models
Accessibility Obtain ability, need for access control, retention
Completeness Row population, column population
Validity
Values in specified range of valid values, values conform to business rule, values conform to other
attributes types & format
Integrity Referential integrity (primary-key/foreign-key match), unique identifier of entity, cardinality
Representation
Easy to read & interpret, presentation language, media appropriate, complete & available
metadata, includes measurement units, consistency in representation
Lineage
Source documentation, segment documentation, target documentation, end-to-end
documentation

10. Common Data Quality Issues
ID Name Address City Postal Code Phone Email
C146 崴 毛 西山台6-20-4 589-0022 0617-555-000 huali@@yaho.com
W123 Durieux SR Conseil 8 rue Ruisseau NANETS 44100 0617555000 mdurieux@gmail.com
R423 Durieu - Prax 8 10 rue Rousseau 44 06175329550 prax@yahoo.com
M979 Mme Charles Durieux R de Courcelles {PARIS}
Accuracy
Not in postal source address
Integrity
Unknown city in
reference table
Consistency
Duplicates
Completeness
Validity
Non-standard formats
Invalid domain
Representation
Non homogeneous
scripts
Lineage Validity
Precision
Missing street #
Validity
Title/Sex rule
Currency
Out of date
Representation
B2C/B2B mixed
contacts

11. Connect to UNDERSTAND
Step 1: Ensure you can access, integrate, virtualize, and synchronize data
from a wide variety of applications and systems to:
• Integrate in batch or real-time with a variety of data sources
• Join and query data from a variety of resources
• Integrate with most REST or SOAP services
(internally developed or external SaaS app)
Ideal for data warehousing, MDM
services, and systems migration
Benefits
Insight
Agility
Efficiency
Flexibility
No matter where your data is, break down silos.

12. Integrate across various sources
Think of all the systems in which
your data (structured &
unstructured) resides (On Prem,
Cloud, ERP, CRM, eCommerce and
marketing automation systems)

13. Discover & Catalog to
MONITOR & MEASURE
• Collaborative business glossary: the glossary sets a foundation for effective data
governance.
• Monitor quality KPIs with scorecarding across your entire stack– based upon
defined business rules
• Lineage and impact analysis: Get all the insights you need on
data origins, movements, traceability, characteristics, and quality.
For more effective business
intelligence, analytics, customer
experience & operational efficiency
Benefits
Manage metadata
Accelerate insight
Facilitate collaboration
Increase transparency
Step 2: Ensure you can classify, locate, tag and profile data
across enterprise

14. Cleanse & Govern
• Collaborative data stewardship: Corrects, approves, and reincorporates
exceptions into the data quality process
• Intelligent data quality rules recommendations based on a transparent ML
matching process for entity resolution
• Get complete and deduplicated data to identify and visualize relationships,
patterns, and trends
• Front-end & native CRM & ERP Connectors: Integrate governance and DQ
processes for Salesforce, SugarCRM, NetSuite, Microsoft Dynamics, and SAP.
Garbage in, garbage out: don’t
jeopardize your downstream
analytics/ decision making
Benefits
Complete data
Smart recommendations
Fast time-to-value
Flexible deployment
Step 3- Standardize, verify, validate and reconcile data across entities.

15. Business User
IT User
Fully Automated
Transparent
White-box
Explainable AI
Readymade rules
Persona oriented
Handles Exception
Quicker
Boost entity resolution with machine learning
Combine machine learning and human expertise
for better matching accuracy at lower costs

16. Add spatial context
• Not just a point on a map – relationships
between locations, logistical networks and
customers
• Geolocation and boundaries extend context
along several dimensions
• Personalization strategies benefit from
understanding customer location and
movement
• Can be used to drive real-time interactions
and offers when customers opt in

17. Scorecard your Data Quality Easily identify all the PIIs that you
have across your company to
accelerate Data Privacy Compliance

18. Deliver trusted data in context across the enterprise
Data
Identity data
Location data
Client data
3rd-party data
Customer engagement
Operational procedures
Analytics – Precisely
owned / 3rd party
Marketing risk
Fraud detection
Data silos Insights
Data management &
location intelligence
KNOWLEDGE GRAPH
DATA INTEGRATION
& FEDERATION
DATA QUALITY
& LOCATION ENRICHMENT
ENTITY RESOLUTION
& CUSTOMER 360

19. Know your data. Improve your outcomes.
Facilitate collaboration
When you can easily map data
lineage, you can locate the
people, places and things that
matter, and see how they’re
connected enterprise-wide
Easy to use and access
A user-friendly web interface, pre-
populated industry assets and
logical modeling help make data
management quick and easy
Accelerate insight
Understand the data you have,
where it resides and how you can
put it to use

20. Data quality
Data integrity capabilities
• Classifies, locates, tags and
profiles data for quick access
and collaboration
• Standardizes, verifies, and
validates data across entities
• Delivers complete, deduplicated,
data to visualize relationships,
patterns, and trends
• Corrects, approves, and
reincorporates exceptions into
the data quality process
Precisely strengths
• Centralized interface for IT and
Business users to manage rules
• Intelligent data quality rules
recommendations based on
machine learning
• Traceability of complete data
asset lifecycle – down to the
column level
• Data stewardship workflow
Data
Enrichment
Location
Intelligence
Data
Quality
Data
Integration

21. The Precisely Data Integrity Suite
• Delivers the essential elements of data integrity –
accuracy, consistency, and context
• Built on data integration, data quality, location
intelligence, and data enrichment trusted by over
12,000 enterprise customers
• Modular architecture allows you to choose just the
capabilities your need – and implement them
alongside your current infrastructure at scale
• Empowers faster, confident decision-making
with trusted data
Data
Integration
Data
Enrichment
Location
Intelligence
Data
Quality

22. Learn more at
precisely.com/data-integrity